The Spyckstraße Bridge, a three-span mixed slab-and-girder structure built in 1976, was assessed to evaluate its current structural condition under modern design codes and increased traffic loads. A short-term structural health monitoring (SHM) campaign was conducted using non-destructive techniques to measure vibrations, displacements, and modal frequencies. These data were used to calibrate and validate a Digital Twin through an iterative genetic algorithm until the difference between experimental and numerical eigenfrequencies was below 3%. The validated Digital Twin was then applied to simulate normative load combinations and determine sectional utilisation ratios across all structural elements. Results confirmed that no component reached ultimate capacity, with the highest tensile demand reaching 97.3% in the central span’s west-side girder under extreme traffic and thermal scenarios. Furthermore, parametric analyses of tendon corrosion revealed significant modal frequency shifts and a reduction in structural capacity, highlighting the potential of Digital Twins for predictive maintenance. This work demonstrates how combining short-term SHM campaigns with calibrated numerical models provides a powerful decision-support tool, enabling early anomaly detection, data-driven maintenance planning, and improved life-cycle management of aging bridge infrastructure.